Quantum optimization applied to the allocation of redundancies in systems in the Oil & Gas industry

The Oil and Gas (O&G) industry demands high system reliability rates. In this context, the Redundancy Allocation Problem (RAP) can be used to seek the ideal redundancy configuration to maximize reliability. Efficient approaches must be explored due to the complexity of these productive systems. This study investigates the optimization of reliability for two systems in the O&G industry by applying the RAP and utilizing quantum algorithms on simulated instances. The cases addressed include the electrical intelligent completion system and the overspeed protection system in gas turbines. Two algorithms are explored: the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). Finally, Despite the current limitations of quantum computing, there is potential for these methods in real applications in the O&G industry.